Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1673
Missing cells285
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory121.5 KiB
Average record size in memory74.4 B

Variable types

Categorical2
Numeric9

Alerts

CE is highly overall correlated with Cond_Seco and 5 other fieldsHigh correlation
Cond_Seco is highly overall correlated with CE and 5 other fieldsHigh correlation
SAL is highly overall correlated with CE and 5 other fieldsHigh correlation
STD_seco is highly overall correlated with CE and 5 other fieldsHigh correlation
Sal_seco is highly overall correlated with CE and 5 other fieldsHigh correlation
X is highly overall correlated with CE and 5 other fieldsHigh correlation
Y is highly overall correlated with CE and 5 other fieldsHigh correlation
Uso_del_Su has 285 (17.0%) missing values Missing
pH_seco has 138 (8.2%) zeros Zeros
Cond_Seco has 135 (8.1%) zeros Zeros
T_Seco has 161 (9.6%) zeros Zeros
STD_seco has 135 (8.1%) zeros Zeros
Sal_seco has 135 (8.1%) zeros Zeros

Reproduction

Analysis started2026-02-24 01:56:28.664865
Analysis finished2026-02-24 01:56:37.125812
Duration8.46 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Tipo_de_Ca
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Aljibe
813 
Pozo
813 
Manantial
 
47

Length

Max length9
Median length6
Mean length5.112373
Min length4

Characters and Unicode

Total characters8553
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPozo
2nd rowAljibe
3rd rowAljibe
4th rowAljibe
5th rowPozo

Common Values

ValueCountFrequency (%)
Aljibe 813
48.6%
Pozo 813
48.6%
Manantial 47
 
2.8%

Length

2026-02-23T20:56:37.179872image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:37.228216image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
ValueCountFrequency (%)
aljibe 813
48.6%
pozo 813
48.6%
manantial 47
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 1626
19.0%
i 860
10.1%
l 860
10.1%
j 813
9.5%
A 813
9.5%
b 813
9.5%
e 813
9.5%
P 813
9.5%
z 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
i 860
10.1%
l 860
10.1%
j 813
9.5%
A 813
9.5%
b 813
9.5%
e 813
9.5%
P 813
9.5%
z 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
i 860
10.1%
l 860
10.1%
j 813
9.5%
A 813
9.5%
b 813
9.5%
e 813
9.5%
P 813
9.5%
z 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
i 860
10.1%
l 860
10.1%
j 813
9.5%
A 813
9.5%
b 813
9.5%
e 813
9.5%
P 813
9.5%
z 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

X
Real number (ℝ)

High correlation 

Distinct1668
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1169876
Minimum1055701
Maximum1320375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:37.304460image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1055701
5-th percentile1109764.8
Q11134128
median1160481.4
Q31191299
95-th percentile1294381.5
Maximum1320375
Range264674
Interquartile range (IQR)57171

Descriptive statistics

Standard deviation51232.999
Coefficient of variation (CV)0.043793529
Kurtosis0.87372975
Mean1169876
Median Absolute Deviation (MAD)27998.38
Skewness1.1147353
Sum1.9572026 × 109
Variance2.6248202 × 109
MonotonicityNot monotonic
2026-02-23T20:56:37.426164image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1177374 3
 
0.2%
1197457 2
 
0.1%
1294386.809 2
 
0.1%
1124841 2
 
0.1%
1299085.737 1
 
0.1%
1298371.675 1
 
0.1%
1298003.653 1
 
0.1%
1299536.07 1
 
0.1%
1218411 1
 
0.1%
1225063 1
 
0.1%
Other values (1658) 1658
99.1%
ValueCountFrequency (%)
1055701 1
0.1%
1058434 1
0.1%
1059661 1
0.1%
1059896 1
0.1%
1065824 1
0.1%
1069055 1
0.1%
1073541 1
0.1%
1074564 1
0.1%
1074799 1
0.1%
1077675 1
0.1%
ValueCountFrequency (%)
1320375 1
0.1%
1318975 1
0.1%
1318190 1
0.1%
1315962 1
0.1%
1315939 1
0.1%
1314192 1
0.1%
1312876 1
0.1%
1312308 1
0.1%
1310445.702 1
0.1%
1309622 1
0.1%

Y
Real number (ℝ)

High correlation 

Distinct1666
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1751737.4
Minimum1646007
Maximum1870665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:37.539339image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1646007
5-th percentile1667749.6
Q11729621.4
median1755844
Q31778759.2
95-th percentile1843099.8
Maximum1870665
Range224658
Interquartile range (IQR)49137.714

Descriptive statistics

Standard deviation47889.209
Coefficient of variation (CV)0.02733812
Kurtosis-0.25663857
Mean1751737.4
Median Absolute Deviation (MAD)25008
Skewness0.0085494095
Sum2.9306567 × 109
Variance2.2933763 × 109
MonotonicityNot monotonic
2026-02-23T20:56:37.644540image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1779724 2
 
0.1%
1834680.992 2
 
0.1%
1788127 2
 
0.1%
1758313 2
 
0.1%
1787531.686 2
 
0.1%
1736801 2
 
0.1%
1844852 2
 
0.1%
1765831 1
 
0.1%
1720222 1
 
0.1%
1769174 1
 
0.1%
Other values (1656) 1656
99.0%
ValueCountFrequency (%)
1646007 1
0.1%
1646601 1
0.1%
1646648 1
0.1%
1647129 1
0.1%
1647612 1
0.1%
1647823 1
0.1%
1649442 1
0.1%
1650340 1
0.1%
1651483 1
0.1%
1651611 1
0.1%
ValueCountFrequency (%)
1870665 1
0.1%
1869611 1
0.1%
1868440 1
0.1%
1868357 1
0.1%
1868225 1
0.1%
1865508 1
0.1%
1861367 1
0.1%
1860211 1
0.1%
1860028 1
0.1%
1858655 1
0.1%

Uso_del_Su
Categorical

Missing 

Distinct12
Distinct (%)0.9%
Missing285
Missing (%)17.0%
Memory size2.1 KiB
Forestal
642 
Ganadería
411 
Forestal-Ganaderia
83 
Otro
72 
Agricultura
 
62
Other values (7)
118 

Length

Max length21
Median length20
Mean length9.4128242
Min length4

Characters and Unicode

Total characters13065
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGanadería
2nd rowGanadería
3rd rowGanadería
4th rowGanadería
5th rowGanadería

Common Values

ValueCountFrequency (%)
Forestal 642
38.4%
Ganadería 411
24.6%
Forestal-Ganaderia 83
 
5.0%
Otro 72
 
4.3%
Agricultura 62
 
3.7%
Urbano 41
 
2.5%
Rancheria-Ganaderia 36
 
2.2%
Agricultura-Ganadería 22
 
1.3%
Agricultura Ganaderia 10
 
0.6%
Forestal-Ganadería 6
 
0.4%
Other values (2) 3
 
0.2%
(Missing) 285
17.0%

Length

2026-02-23T20:56:37.884754image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestal 645
46.0%
ganadería 411
29.3%
forestal-ganaderia 83
 
5.9%
agricultura 73
 
5.2%
otro 72
 
5.1%
urbano 41
 
2.9%
rancheria-ganaderia 36
 
2.6%
agricultura-ganadería 22
 
1.6%
ganaderia 12
 
0.9%
forestal-ganadería 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 2652
20.3%
r 1643
12.6%
e 1340
10.3%
t 901
 
6.9%
o 847
 
6.5%
l 829
 
6.3%
F 734
 
5.6%
s 734
 
5.6%
n 647
 
5.0%
G 570
 
4.4%
Other values (14) 2168
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2652
20.3%
r 1643
12.6%
e 1340
10.3%
t 901
 
6.9%
o 847
 
6.5%
l 829
 
6.3%
F 734
 
5.6%
s 734
 
5.6%
n 647
 
5.0%
G 570
 
4.4%
Other values (14) 2168
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2652
20.3%
r 1643
12.6%
e 1340
10.3%
t 901
 
6.9%
o 847
 
6.5%
l 829
 
6.3%
F 734
 
5.6%
s 734
 
5.6%
n 647
 
5.0%
G 570
 
4.4%
Other values (14) 2168
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2652
20.3%
r 1643
12.6%
e 1340
10.3%
t 901
 
6.9%
o 847
 
6.5%
l 829
 
6.3%
F 734
 
5.6%
s 734
 
5.6%
n 647
 
5.0%
G 570
 
4.4%
Other values (14) 2168
16.6%

pH_seco
Real number (ℝ)

Zeros 

Distinct237
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7342977
Minimum0
Maximum9.56
Zeros138
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:37.991711image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.94
median7.26
Q37.61
95-th percentile8.124
Maximum9.56
Range9.56
Interquartile range (IQR)0.67

Descriptive statistics

Standard deviation2.0748129
Coefficient of variation (CV)0.30809641
Kurtosis6.2351945
Mean6.7342977
Median Absolute Deviation (MAD)0.34
Skewness-2.7581763
Sum11266.48
Variance4.3048487
MonotonicityNot monotonic
2026-02-23T20:56:38.101706image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
 
8.2%
7.26 25
 
1.5%
7.56 21
 
1.3%
6.95 19
 
1.1%
7.3 18
 
1.1%
7.24 17
 
1.0%
7.36 17
 
1.0%
7.13 17
 
1.0%
7.17 17
 
1.0%
7.35 16
 
1.0%
Other values (227) 1368
81.8%
ValueCountFrequency (%)
0 138
8.2%
4.19 1
 
0.1%
4.95 1
 
0.1%
5.35 1
 
0.1%
5.41 1
 
0.1%
5.53 1
 
0.1%
5.56 1
 
0.1%
5.61 1
 
0.1%
5.73 1
 
0.1%
5.88 1
 
0.1%
ValueCountFrequency (%)
9.56 1
 
0.1%
9.49 1
 
0.1%
9.47 2
0.1%
8.86 1
 
0.1%
8.74 1
 
0.1%
8.72 3
0.2%
8.59 1
 
0.1%
8.57 1
 
0.1%
8.53 1
 
0.1%
8.52 1
 
0.1%

Cond_Seco
Real number (ℝ)

High correlation  Zeros 

Distinct1387
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2914.2934
Minimum0
Maximum59830
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:38.204548image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1831.5
median1542
Q33223
95-th percentile9665
Maximum59830
Range59830
Interquartile range (IQR)2391.5

Descriptive statistics

Standard deviation4529.7646
Coefficient of variation (CV)1.5543269
Kurtosis43.323775
Mean2914.2934
Median Absolute Deviation (MAD)913.8
Skewness5.4194459
Sum4875612.8
Variance20518767
MonotonicityNot monotonic
2026-02-23T20:56:38.315032image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
1006 5
 
0.3%
1101 4
 
0.2%
1137 3
 
0.2%
1148 3
 
0.2%
1240 3
 
0.2%
2546 3
 
0.2%
1316 3
 
0.2%
1218 3
 
0.2%
1409 3
 
0.2%
Other values (1377) 1508
90.1%
ValueCountFrequency (%)
0 135
8.1%
58.31 1
 
0.1%
68.43 1
 
0.1%
104.5 1
 
0.1%
131.4 1
 
0.1%
155.7 1
 
0.1%
172.5 1
 
0.1%
173.7 1
 
0.1%
177.4 1
 
0.1%
221.3 1
 
0.1%
ValueCountFrequency (%)
59830 1
0.1%
51260 1
0.1%
47700 1
0.1%
41490 1
0.1%
38850 1
0.1%
36830 1
0.1%
36050 1
0.1%
35440 1
0.1%
33330 1
0.1%
33030 1
0.1%

T_Seco
Real number (ℝ)

Zeros 

Distinct106
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.917215
Minimum0
Maximum41.4
Zeros161
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:38.424595image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.1
median30.9
Q332.3
95-th percentile33.7
Maximum41.4
Range41.4
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation9.3437037
Coefficient of variation (CV)0.33469327
Kurtosis4.7370061
Mean27.917215
Median Absolute Deviation (MAD)1.5
Skewness-2.5043396
Sum46705.5
Variance87.304799
MonotonicityNot monotonic
2026-02-23T20:56:38.534068image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 161
 
9.6%
25 60
 
3.6%
32.4 39
 
2.3%
30.7 39
 
2.3%
31.2 38
 
2.3%
31.1 37
 
2.2%
32.6 36
 
2.2%
30.3 36
 
2.2%
31.8 35
 
2.1%
32.3 35
 
2.1%
Other values (96) 1157
69.2%
ValueCountFrequency (%)
0 161
9.6%
20.2 2
 
0.1%
21.9 1
 
0.1%
22.8 1
 
0.1%
24.5 1
 
0.1%
25 60
 
3.6%
25.4 2
 
0.1%
25.5 1
 
0.1%
25.7 1
 
0.1%
26.3 1
 
0.1%
ValueCountFrequency (%)
41.4 1
 
0.1%
38.7 1
 
0.1%
37.6 2
0.1%
37 1
 
0.1%
36.8 1
 
0.1%
36.6 1
 
0.1%
36.1 2
0.1%
35.5 1
 
0.1%
35.4 3
0.2%
35.3 1
 
0.1%

STD_seco
Real number (ℝ)

High correlation  Zeros 

Distinct1437
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1431.7049
Minimum0
Maximum29320
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:38.634217image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1405.7
median751.4
Q31596
95-th percentile4941.1894
Maximum29320
Range29320
Interquartile range (IQR)1190.3

Descriptive statistics

Standard deviation2206.869
Coefficient of variation (CV)1.5414273
Kurtosis42.613753
Mean1431.7049
Median Absolute Deviation (MAD)448
Skewness5.3145219
Sum2395242.3
Variance4870270.9
MonotonicityNot monotonic
2026-02-23T20:56:38.754484image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
1950 3
 
0.2%
578 3
 
0.2%
1284 3
 
0.2%
1044 3
 
0.2%
268.4 3
 
0.2%
774.9 2
 
0.1%
1259 2
 
0.1%
263.3 2
 
0.1%
1240 2
 
0.1%
Other values (1427) 1515
90.6%
ValueCountFrequency (%)
0 135
8.1%
1.016 1
 
0.1%
1.052 1
 
0.1%
1.255 1
 
0.1%
2.487 1
 
0.1%
7.54 1
 
0.1%
29.07 1
 
0.1%
33.37 1
 
0.1%
51.72 1
 
0.1%
64.87 1
 
0.1%
ValueCountFrequency (%)
29320 1
0.1%
25139.7744 1
0.1%
23370 1
0.1%
20330 1
0.1%
19030 1
0.1%
17670 1
0.1%
17370 1
0.1%
16330 1
0.1%
16190 1
0.1%
13760 1
0.1%

Sal_seco
Real number (ℝ)

High correlation  Zeros 

Distinct1194
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6735387
Minimum0
Maximum59.8
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:38.862448image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.457
median0.832
Q31.755
95-th percentile5.5564
Maximum59.8
Range59.8
Interquartile range (IQR)1.298

Descriptive statistics

Standard deviation3.0990628
Coefficient of variation (CV)1.8518024
Kurtosis106.95875
Mean1.6735387
Median Absolute Deviation (MAD)0.483
Skewness8.2095819
Sum2799.8302
Variance9.6041904
MonotonicityNot monotonic
2026-02-23T20:56:38.964296image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
0.357 6
 
0.4%
0.603 5
 
0.3%
0.398 5
 
0.3%
0.702 5
 
0.3%
0.415 4
 
0.2%
0.452 4
 
0.2%
0.464 4
 
0.2%
0.666 4
 
0.2%
0.708 4
 
0.2%
Other values (1184) 1497
89.5%
ValueCountFrequency (%)
0 135
8.1%
0.085 1
 
0.1%
0.086 1
 
0.1%
0.089 1
 
0.1%
0.104 1
 
0.1%
0.117 1
 
0.1%
0.122 1
 
0.1%
0.135 1
 
0.1%
0.136 1
 
0.1%
0.137 1
 
0.1%
ValueCountFrequency (%)
59.8 1
0.1%
40.36 1
0.1%
31.13 1
0.1%
28.193 1
0.1%
26.76 1
0.1%
24.85 1
0.1%
23.47 1
0.1%
22.91 1
0.1%
22.51 1
0.1%
21 1
0.1%

CE
Real number (ℝ)

High correlation 

Distinct1494
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2998.9801
Minimum58.31
Maximum59830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:39.066029image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum58.31
5-th percentile464.36
Q1921.1
median1616
Q33256
95-th percentile9665
Maximum59830
Range59771.69
Interquartile range (IQR)2334.9

Descriptive statistics

Standard deviation4490.4965
Coefficient of variation (CV)1.4973412
Kurtosis44.536219
Mean2998.9801
Median Absolute Deviation (MAD)877.7
Skewness5.5199764
Sum5017293.6
Variance20164559
MonotonicityNot monotonic
2026-02-23T20:56:39.174432image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006 5
 
0.3%
1101 4
 
0.2%
1911 3
 
0.2%
1307 3
 
0.2%
1864 3
 
0.2%
1115 3
 
0.2%
1447 3
 
0.2%
1240 3
 
0.2%
1218 3
 
0.2%
1137 3
 
0.2%
Other values (1484) 1640
98.0%
ValueCountFrequency (%)
58.31 1
0.1%
68.43 1
0.1%
104.5 1
0.1%
114.1 1
0.1%
126.5 1
0.1%
131.4 1
0.1%
155.7 1
0.1%
172.5 1
0.1%
173.7 1
0.1%
177.4 1
0.1%
ValueCountFrequency (%)
59830 1
0.1%
51260 1
0.1%
47700 1
0.1%
41490 1
0.1%
38850 1
0.1%
36830 1
0.1%
36050 1
0.1%
35440 1
0.1%
33330 1
0.1%
33030 1
0.1%

SAL
Real number (ℝ)

High correlation 

Distinct1259
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7216967
Minimum0.063
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:39.283963image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0.063
5-th percentile0.2686
Q10.504
median0.88
Q31.782
95-th percentile5.5564
Maximum59.8
Range59.737
Interquartile range (IQR)1.278

Descriptive statistics

Standard deviation3.080298
Coefficient of variation (CV)1.7891061
Kurtosis109.1349
Mean1.7216967
Median Absolute Deviation (MAD)0.469
Skewness8.3215863
Sum2880.3985
Variance9.4882358
MonotonicityNot monotonic
2026-02-23T20:56:39.534655image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 6
 
0.4%
0.535 5
 
0.3%
0.702 5
 
0.3%
0.49 5
 
0.3%
0.452 5
 
0.3%
0.398 5
 
0.3%
0.603 5
 
0.3%
0.797 4
 
0.2%
0.359 4
 
0.2%
0.457 4
 
0.2%
Other values (1249) 1625
97.1%
ValueCountFrequency (%)
0.063 1
0.1%
0.07 1
0.1%
0.085 1
0.1%
0.086 1
0.1%
0.089 1
0.1%
0.099 1
0.1%
0.104 1
0.1%
0.112 2
0.1%
0.117 2
0.1%
0.121 1
0.1%
ValueCountFrequency (%)
59.8 1
0.1%
40.36 1
0.1%
31.13 1
0.1%
28.193 1
0.1%
26.76 1
0.1%
24.85 1
0.1%
23.47 1
0.1%
22.91 1
0.1%
22.51 1
0.1%
21 1
0.1%

Interactions

2026-02-23T20:56:36.137213image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.052424image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.154656image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.105853image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.003578image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.823927image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.578281image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.452189image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.342291image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.218870image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.175887image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.274651image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.192420image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.094674image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.911705image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.667154image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.534971image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.430575image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.306446image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.309065image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.388069image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.282505image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.193150image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.001857image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.747359image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.612294image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.514577image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.392095image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.429233image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.494854image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.362472image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.292519image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.084270image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.832219image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.694475image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.601107image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.482185image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.542378image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.616879image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.446875image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.383664image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.173979image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.924857image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.780268image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.684349image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.566257image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.664994image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.714619image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.530231image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.464562image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.244530image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.009550image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.857283image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.772291image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.660670image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.785952image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.818027image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.615169image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.552066image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.334380image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.137721image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.944470image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.856343image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.743663image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:29.909801image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.921031image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.696548image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.647064image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.414685image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.265176image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.030910image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.955225image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.832066image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:30.034571image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.014591image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:31.782223image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:32.732246image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:33.500370image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:34.364284image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:35.118348image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:36.051013image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Correlations

2026-02-23T20:56:39.616760image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
CECond_SecoSALSTD_secoSal_secoT_SecoTipo_de_CaUso_del_SuXYpH_seco
CE1.0000.9480.9870.9300.9380.3620.1120.0000.5010.6040.169
Cond_Seco0.9481.0000.9350.9830.9910.4480.1110.0000.5510.6570.267
SAL0.9870.9351.0000.9300.9440.3600.1010.0000.5100.6070.160
STD_seco0.9300.9830.9301.0000.9870.4410.1140.0000.5510.6570.263
Sal_seco0.9380.9910.9440.9871.0000.4490.1020.0000.5620.6630.263
T_Seco0.3620.4480.3600.4410.4491.0000.2270.2360.3180.4830.359
Tipo_de_Ca0.1120.1110.1010.1140.1020.2271.0000.2050.2550.3130.170
Uso_del_Su0.0000.0000.0000.0000.0000.2360.2051.0000.2430.2740.263
X0.5010.5510.5100.5510.5620.3180.2550.2431.0000.7240.159
Y0.6040.6570.6070.6570.6630.4830.3130.2740.7241.0000.405
pH_seco0.1690.2670.1600.2630.2630.3590.1700.2630.1590.4051.000

Missing values

2026-02-23T20:56:36.964724image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-23T20:56:37.073875image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Tipo_de_CaXYUso_del_SupH_secoCond_SecoT_SecoSTD_secoSal_secoCESAL
0Pozo1299675.9701827467.800NaN7.503060.031.21500.7356551.683003060.01.6830
1Aljibe1299085.7371828796.878NaN7.121585.031.4777.3418340.871751585.00.8718
2Aljibe1298371.6751829001.292NaN7.07995.032.7487.9843060.54725995.00.5473
3Aljibe1298003.6531829238.533NaN7.371339.033.3656.6944580.736451339.00.7365
4Pozo1299536.0701826712.393NaN7.691553.00.0761.6478670.854151553.00.8541
5Manantial1289335.0541822647.460NaN7.293110.030.11525.2574791.710503110.01.7105
6Aljibe1283846.2151805206.624NaN6.752417.031.61185.3849931.329352417.01.3294
7Aljibe1292863.7001800658.661NaN7.928690.031.64261.8930854.779508690.04.7795
8Aljibe1293911.4011833578.534NaN6.851205.030.6590.9759690.662751205.00.6627
9Manantial1293490.7601834412.838NaN8.091429.029.7700.8337420.785951429.00.7860
Tipo_de_CaXYUso_del_SupH_secoCond_SecoT_SecoSTD_secoSal_secoCESAL
1663Manantial1119023.01657606.0Forestal0.00.00.00.00.0337.90.213
1664Manantial1117405.01657727.0Forestal0.00.00.00.00.0479.80.282
1665Pozo1117152.01657560.0Ganadería0.00.00.00.00.01050.00.570
1666Manantial1117068.01650340.0Forestal0.00.00.00.00.0196.00.643
1667Manantial1114278.01647612.0Forestal0.00.00.00.00.0387.70.237
1668Aljibe1112794.01649442.0Forestal0.00.00.00.00.01561.00.835
1669Manantial1118157.01656141.0Forestal0.00.00.00.00.0290.30.190
1670Manantial1127604.01669373.0Forestal0.00.00.00.00.0510.20.297
1671Pozo1125437.01669834.0Agricultura0.00.00.00.00.0656.20.371
1672Pozo1138629.01713505.0Forestal0.00.00.00.00.0734.60.409